1. Field of the Invention
The present invention relates to fire flame detection technology. More particularly, the present invention relates to a fire-flame detection method that makes a model of visual features based on a probabilistic membership function and applies it to fuzzy finite automata (FFA), thereby remarkably enhancing the performance of detecting a fire flame.
2. Description of the Related Art
In recent years, various accidents have frequently occurred due to terrorism or natural disasters. This leads to the need for monitoring public places. As fire may endanger the lives of many people and cause economic damage. To prevent accidents, the development of an automatic fire-flame detection system is required.
Most conventional fire warning systems operate using physical or chemical characteristics regarding fire, such as, smoke, heat, radiation, etc., by infrared sensors, optical sensors, icon sensors, etc. Such conventional systems cannot operate until the sensors sense smoke or heat, for example. The systems cannot provide addition information such as the location where a fire occurred, the degree of spread of the fire, etc., either. Therefore, although the systems create a fire warning, the system managers must directly move to the fire site and detect whether a fire really occurred with the naked eye.
In order to resolve the problems in conventional sensor-based fire warning systems and to provide the fire detection result with a relatively high degree of reliability, systems have been developed to employ a CCD camera. For example, S. Y. Foo proposed a fuzzy logic approach that detects a fire in an air-plane dry bay and engine compartments by a set of statistical measures derived from histograms and by image subtraction from consecutive image frames. B. U. Toreyin proposed a method for verifying a fire by detecting moving regions via a background model and by measuring the variation of wavelet radio frequency count for candidate regions in temporal/spatial domain and a fire-colored model for candidate regions. C-C Ho proposed a system that detects and alarms in real-time frames and smoke using the spectral, spatial and temporal features. The statistical distribution of the spectral and spatial probability density serves as a weight in a fuzzy logic derivation system for detecting flame and smoke regions. Continuously adaptive mean shift (CAMSHIFT) is used to provide feedback on the real-time position of flames and smoke.
T. Chen proposed a method that analyzes a fire via an RGB/HIS color model and information regarding the disorder spread of frame regions. D. Han proposed a system that detects fires and smoke in a tunnel by variations in color and movement. T. Celik proposed a method for applying the difference between Y, Cb, and Cr in YCbCr color space to a fuzzy logic.
In addition, a Support Vector Machine (SVM)-based fire detection method is also proposed; however, this requires additional computation time according to the number and dimensions of the support vectors. Although the SVM-based fire detection method has better detection performance than other fire detection methods, it is not suitable for real-time detection applications.
As such, many conventional systems have been researched and developed; however they have not detected fire flames with a sufficient level of precision because of the characteristics of fire flames, i.e., the continuous and disordered patterns.